Adjusted docs to reflect move of qs nb + finetuning script into examples

docs/multi_gpu.md

@@ -24,7 +24,7 @@ This runs with the `samsum_dataset` for summarization application by default.
 
 ```bash
 
-torchrun --nnodes 1 --nproc_per_node 4  llama_finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --output_dir Path/to/save/PEFT/model
+torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --output_dir Path/to/save/PEFT/model
 
 ```
 
@@ -43,7 +43,7 @@ We use `torchrun` here to spawn multiple processes for FSDP.
 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 --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --output_dir Path/to/save/PEFT/model --use_fast_kernels
+torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --output_dir Path/to/save/PEFT/model --use_fast_kernels
 ```
 
 ### Fine-tuning using FSDP Only
@@ -52,7 +52,7 @@ If interested in running full parameter finetuning without making use of PEFT me
 
 ```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 --pure_bf16 --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 --pure_bf16 --use_fast_kernels
 
 ```
 
@@ -62,7 +62,7 @@ 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
 
 ```
 
@@ -72,7 +72,7 @@ Here we use a slurm script to schedule a job with slurm over multiple nodes.
 
 ```bash
 
-sbatch multi_node.slurm
+sbatch examples/multi_node.slurm
 # Change the num nodes and GPU per nodes in the script before running.
 
 ```
@@ -95,16 +95,16 @@ To run with each of the datasets set the `dataset` flag in the command as shown
 
 ```bash
 # grammer_dataset
-torchrun --nnodes 1 --nproc_per_node 4  llama_finetuning.py --enable_fsdp  --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --dataset grammar_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned  --pure_bf16 --output_dir Path/to/save/PEFT/model
+torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp  --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --dataset grammar_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned  --pure_bf16 --output_dir Path/to/save/PEFT/model
 
 # alpaca_dataset
 
-torchrun --nnodes 1 --nproc_per_node 4  llama_finetuning.py --enable_fsdp  --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --dataset alpaca_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16 --output_dir Path/to/save/PEFT/model
+torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp  --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --dataset alpaca_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16 --output_dir Path/to/save/PEFT/model
 
 
 # samsum_dataset
 
-torchrun --nnodes 1 --nproc_per_node 4  llama_finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --dataset samsum_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16 --output_dir Path/to/save/PEFT/model
+torchrun --nnodes 1 --nproc_per_node 4  examples/finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --use_peft --peft_method lora --dataset samsum_dataset --save_model --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16 --output_dir Path/to/save/PEFT/model
 
 ```
 

examples/README.md

@@ -1,6 +1,20 @@
-# Inference
+# Examples
 
-This folder contains inference examples for Llama 2. So far, we have provided support for three methods of inference:
+This folder contains finetuning and inference examples for Llama 2.
+
+## Finetuning
+
+Please refer to the main [README.md](../README.md) for information on how to use the [finetuning.py](./finetuning.py) script.
+After installing the llama-recipes package through [pip](../README.md#installation) you can also invoke the finetuning in two ways:
+```
+python -m llama_recipes.finetuning <parameters>
+
+python examnples/finetuning.py <parameters>
+```
+Please see [README.md](../README.md) for details.
+
+## Inference 
+So far, we have provided support for three methods of inference:
 
 1. [inference script](./inference.py) script provides support for Hugging Face accelerate, PEFT and FSDP fine tuned models.
 

examples/multi_node.slurm

@@ -32,5 +32,5 @@ export CUDA_LAUNCH_BLOCKING=0
 export NCCL_SOCKET_IFNAME="ens"
 export FI_EFA_USE_DEVICE_RDMA=1
 
-srun  torchrun --nproc_per_node 4 --rdzv_id $RANDOM --rdzv_backend c10d --rdzv_endpoint $head_node_ip:29500 llama_finetuning.py  --enable_fsdp --use_peft --peft_method lora
+srun  torchrun --nproc_per_node 4 --rdzv_id $RANDOM --rdzv_backend c10d --rdzv_endpoint $head_node_ip:29500 examples/finetuning.py  --enable_fsdp --use_peft --peft_method lora