Update stale recipes/quickstart paths to getting-started

end-to-end-use-cases/RAFT-Chatbot/README.md

@@ -121,7 +121,7 @@ Once the RAFT dataset is ready in JSON format, we can start fine-tuning. Unfortu
 ```bash
 export PATH_TO_ROOT_FOLDER=./raft-8b
 export PATH_TO_RAFT_JSON=recipes/use_cases/end2end-recipes/raft/output/raft.jsonl
-torchrun --nnodes 1 --nproc_per_node 4  recipes/quickstart/finetuning/finetuning.py --enable_fsdp --lr 1e-5 --context_length 8192 --num_epochs 1 --batch_size_training 1 --model_name meta-Llama/Meta-Llama-3-8B-Instruct --dist_checkpoint_root_folder $PATH_TO_ROOT_FOLDER --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "recipes/finetuning/datasets/raft_dataset.py" --use-wandb  --run_validation True  --custom_dataset.data_path $PATH_TO_RAFT_JSON
+torchrun --nnodes 1 --nproc_per_node 4  getting-started/finetuning/finetuning.py --enable_fsdp --lr 1e-5 --context_length 8192 --num_epochs 1 --batch_size_training 1 --model_name meta-Llama/Meta-Llama-3-8B-Instruct --dist_checkpoint_root_folder $PATH_TO_ROOT_FOLDER --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "getting-started/finetuning/datasets/raft_dataset.py" --use-wandb  --run_validation True  --custom_dataset.data_path $PATH_TO_RAFT_JSON
 ```
 
 For more details on multi-GPU fine-tuning, please refer to the [multigpu_finetuning.md](../../getting-started/finetuning/multigpu_finetuning.md) in the finetuning recipe.

getting-started/finetuning/datasets/README.md

@@ -1,6 +1,6 @@
 # Datasets and Evaluation Metrics
 
-The provided fine tuning scripts allows you to select between three datasets by passing the `dataset` arg to the `llama_cookbook.finetuning` module or [`recipes/quickstart/finetuning/finetuning.py`](../finetuning.py) script. The current options are `grammar_dataset`, `alpaca_dataset`and `samsum_dataset`. Additionally, we integrate the OpenAssistant/oasst1 dataset as an [example for a custom dataset](custom_dataset.py) 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)
+The provided fine tuning scripts allows you to select between three datasets by passing the `dataset` arg to the `llama_cookbook.finetuning` module or [`getting-started/finetuning/finetuning.py`](../finetuning.py) script. The current options are `grammar_dataset`, `alpaca_dataset`and `samsum_dataset`. Additionally, we integrate the OpenAssistant/oasst1 dataset as an [example for a custom dataset](custom_dataset.py) 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)
 
 * [grammar_dataset](https://huggingface.co/datasets/jfleg) contains 150K pairs of english sentences and possible corrections.
 * [alpaca_dataset](https://github.com/tatsu-lab/stanford_alpaca) provides 52K instruction-response pairs as generated by `text-davinci-003`.

getting-started/finetuning/finetune_vision_model.md

@@ -10,19 +10,19 @@ We created an example script [ocrvqa_dataset.py](./datasets/ocrvqa_dataset.py) t
 For **full finetuning with FSDP**, we can run the following code:
 
 ```bash
-  torchrun --nnodes 1 --nproc_per_node 4  recipes/quickstart/finetuning/finetuning.py --enable_fsdp --lr 1e-5  --num_epochs 3 --batch_size_training 2 --model_name meta-llama/Llama-3.2-11B-Vision-Instruct --dist_checkpoint_root_folder ./finetuned_model --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "recipes/quickstart/finetuning/datasets/ocrvqa_dataset.py"  --run_validation True --batching_strategy padding
+  torchrun --nnodes 1 --nproc_per_node 4  getting-started/finetuning/finetuning.py --enable_fsdp --lr 1e-5  --num_epochs 3 --batch_size_training 2 --model_name meta-llama/Llama-3.2-11B-Vision-Instruct --dist_checkpoint_root_folder ./finetuned_model --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "getting-started/finetuning/datasets/ocrvqa_dataset.py"  --run_validation True --batching_strategy padding
 ```
 
 For **LoRA finetuning with FSDP**, we can run the following code:
 
 ```bash
-  torchrun --nnodes 1 --nproc_per_node 4  recipes/quickstart/finetuning/finetuning.py --enable_fsdp --lr 1e-5  --num_epochs 3 --batch_size_training 2 --model_name meta-llama/Llama-3.2-11B-Vision-Instruct --dist_checkpoint_root_folder ./finetuned_model --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "recipes/quickstart/finetuning/datasets/ocrvqa_dataset.py"  --run_validation True --batching_strategy padding  --use_peft --peft_method lora
+  torchrun --nnodes 1 --nproc_per_node 4  getting-started/finetuning/finetuning.py --enable_fsdp --lr 1e-5  --num_epochs 3 --batch_size_training 2 --model_name meta-llama/Llama-3.2-11B-Vision-Instruct --dist_checkpoint_root_folder ./finetuned_model --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "getting-started/finetuning/datasets/ocrvqa_dataset.py"  --run_validation True --batching_strategy padding  --use_peft --peft_method lora
 ```
 
 For **finetuning with LLM freeze using FSDP**, we can run the following code:
 
 ```bash
-  torchrun --nnodes 1 --nproc_per_node 4  recipes/quickstart/finetuning/finetuning.py --enable_fsdp --lr 1e-5  --num_epochs 3 --batch_size_training 2 --model_name meta-llama/Llama-3.2-11B-Vision-Instruct --dist_checkpoint_root_folder ./finetuned_model --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "recipes/quickstart/finetuning/datasets/ocrvqa_dataset.py"  --run_validation True --batching_strategy padding --freeze_LLM_only True
+  torchrun --nnodes 1 --nproc_per_node 4  getting-started/finetuning/finetuning.py --enable_fsdp --lr 1e-5  --num_epochs 3 --batch_size_training 2 --model_name meta-llama/Llama-3.2-11B-Vision-Instruct --dist_checkpoint_root_folder ./finetuned_model --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "getting-started/finetuning/datasets/ocrvqa_dataset.py"  --run_validation True --batching_strategy padding --freeze_LLM_only True
 ```
 **Note**: `--batching_strategy padding` is needed as the vision model will not work with `packing` method.
 
@@ -34,7 +34,7 @@ For more details about local inference with the fine-tuned checkpoint, please re
 
 In order to use a custom dataset, please follow the steps below:
 
-1. Create a new dataset python file under `recipes/quickstart/finetuning/dataset` folder.
+1. Create a new dataset python file under `getting-started/finetuning/datasets` folder.
 2. In this python file, you need to define a `get_custom_dataset(dataset_config, processor, split, split_ratio=0.9)` function that handles the data loading.
 3. In this python file, you need to define a `get_data_collator(processor)` function that returns a custom data collator that can be used by the Pytorch Data Loader.
 4. This custom data collator class must have a `__call__(self, samples)` function that converts the image and text samples into the actual inputs that vision model expects.

getting-started/finetuning/multigpu_finetuning.md

@@ -74,7 +74,7 @@ Here we use a slurm script to schedule a job with slurm over multiple nodes.
 
 ```bash
 
-sbatch recipes/quickstart/finetuning/multi_node.slurm
+sbatch getting-started/finetuning/multi_node.slurm
 # Change the num nodes and GPU per nodes in the script before running.
 
 ```

getting-started/inference/local_inference/README.md

@@ -111,7 +111,7 @@ In case you have fine-tuned your model with pure FSDP and saved the checkpoints
 This is helpful if you have fine-tuned you model using FSDP only as follows:
 
 ```bash
-torchrun --nnodes 1 --nproc_per_node 8  recipes/quickstart/finetuning/finetuning.py --enable_fsdp --model_name /path_of_model_folder/7B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --fsdp_config.pure_bf16
+torchrun --nnodes 1 --nproc_per_node 8  getting-started/finetuning/finetuning.py --enable_fsdp --model_name /path_of_model_folder/7B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --fsdp_config.pure_bf16
 ```
 Then convert your FSDP checkpoint to HuggingFace checkpoints using:
 ```bash