update more links

getting-started/finetuning/README.md

@@ -6,7 +6,7 @@ This folder contains instructions to fine-tune Meta Llama 3 on a
 * [single-GPU setup](./singlegpu_finetuning.md)
 * [multi-GPU setup](./multigpu_finetuning.md)
 
-using the canonical [finetuning script](../../src/llama_recipes/finetuning.py) in the llama-recipes package.
+using the canonical [finetuning script](../../src/llama_cookbook/finetuning.py) in the llama-cookbook package.
 
 If you are new to fine-tuning techniques, check out [an overview](./LLM_finetuning_overview.md).
 
@@ -17,10 +17,10 @@ If you are new to fine-tuning techniques, check out [an overview](./LLM_finetuni
 ## How to configure finetuning settings?
 
 > [!TIP]
-> All the setting defined in [config files](../../src/llama_recipes/configs/) can be passed as args through CLI when running the script, there is no need to change from config files directly.
+> All the setting defined in [config files](../../src/llama_cookbook/configs/) can be passed as args through CLI when running the script, there is no need to change from config files directly.
 
 
-* [Training config file](../../src/llama_recipes/configs/training.py) is the main config file that helps to specify the settings for our run and can be found in [configs folder](../../src/llama_recipes/configs/)
+* [Training config file](../../src/llama_cookbook/configs/training.py) is the main config file that helps to specify the settings for our run and can be found in [configs folder](../../src/llama_cookbook/configs/)
 
 It lets us specify the training settings for everything from `model_name` to `dataset_name`, `batch_size` and so on. Below is the list of supported settings:
 
@@ -71,11 +71,11 @@ It lets us specify the training settings for everything from `model_name` to `da
 
 ```
 
-* [Datasets config file](../../src/llama_recipes/configs/datasets.py) provides the available options for datasets.
+* [Datasets config file](../../src/llama_cookbook/configs/datasets.py) provides the available options for datasets.
 
-* [peft config file](../../src/llama_recipes/configs/peft.py) provides the supported PEFT methods and respective settings that can be modified. We currently support LoRA and Llama-Adapter. Please note that LoRA is the only technique which is supported in combination with FSDP.
+* [peft config file](../../src/llama_cookbook/configs/peft.py) provides the supported PEFT methods and respective settings that can be modified. We currently support LoRA and Llama-Adapter. Please note that LoRA is the only technique which is supported in combination with FSDP.
 
-* [FSDP config file](../../src/llama_recipes/configs/fsdp.py) provides FSDP settings such as:
+* [FSDP config file](../../src/llama_cookbook/configs/fsdp.py) provides FSDP settings such as:
 
     * `mixed_precision` boolean flag to specify using mixed precision, defatults to true.
 
@@ -102,7 +102,7 @@ It lets us specify the training settings for everything from `model_name` to `da
 You can enable [W&B](https://wandb.ai/) experiment tracking by using `use_wandb` flag as below. You can change the project name, entity and other `wandb.init` arguments in `wandb_config`.
 
 ```bash
-python -m llama_recipes.finetuning --use_peft --peft_method lora --quantization 8bit --model_name /path_of_model_folder/8B --output_dir Path/to/save/PEFT/model --use_wandb
+python -m llama_cookbook.finetuning --use_peft --peft_method lora --quantization 8bit --model_name /path_of_model_folder/8B --output_dir Path/to/save/PEFT/model --use_wandb
 ```
 You'll be able to access a dedicated project or run link on [wandb.ai](https://wandb.ai) and see your dashboard like the one below.
 <div style="display: flex;">

getting-started/finetuning/multigpu_finetuning.md

@@ -3,14 +3,14 @@ This recipe steps you through how to finetune a Meta Llama 3 model on the text s
 
 
 ## Requirements
-Ensure that you have installed the llama-recipes package ([details](../../README.md#installing)).
+Ensure that you have installed the llama-cookbook package ([details](../../README.md#installing)).
 
 We will also need 2 packages:
 1. [PEFT](https://github.com/huggingface/peft) to use parameter-efficient finetuning.
 2. [FSDP](https://pytorch.org/tutorials/intermediate/FSDP_adavnced_tutorial.html) which helps us parallelize the training over multiple GPUs. [More details](./LLM_finetuning_overview.md#2-full-partial-parameter-finetuning).
 
 > [!NOTE]
-> The llama-recipes package will install PyTorch 2.0.1 version. In case you want to use FSDP with PEFT for multi GPU finetuning, please install the PyTorch nightlies ([details](../../README.md#pytorch-nightlies))
+> The llama-cookbook package will install PyTorch 2.0.1 version. In case you want to use FSDP with PEFT for multi GPU finetuning, please install the PyTorch nightlies ([details](../../README.md#pytorch-nightlies))
 >
 > INT8 quantization is not currently supported in FSDP
 
@@ -96,14 +96,14 @@ srun  torchrun --nproc_per_node 8 --rdzv_id $RANDOM --rdzv_backend c10d --rdzv_e
 Do not forget to adjust the number of nodes, ntasks and gpus-per-task in the top.
 
 ## Running with different datasets
-Currently 3 open source datasets are supported that can be found in [Datasets config file](../../src/llama_recipes/configs/datasets.py). You can also use your custom dataset (more info [here](./datasets/README.md)).
+Currently 3 open source datasets are supported that can be found in [Datasets config file](../../src/llama_cookbook/configs/datasets.py). You can also use your custom dataset (more info [here](./datasets/README.md)).
 
-* `grammar_dataset` : use this [notebook](../../src/llama_recipes/datasets/grammar_dataset/grammar_dataset_process.ipynb) to pull and process the Jfleg and C4 200M datasets for grammar checking.
+* `grammar_dataset` : use this [notebook](../../src/llama_cookbook/datasets/grammar_dataset/grammar_dataset_process.ipynb) to pull and process the Jfleg and C4 200M datasets for grammar checking.
 
 * `alpaca_dataset` : to get this open source data please download the `aplaca.json` to `dataset` folder.
 
 ```bash
-wget -P ../../src/llama_recipes/datasets https://raw.githubusercontent.com/tatsu-lab/stanford_alpaca/main/alpaca_data.json
+wget -P ../../src/llama_cookbook/datasets https://raw.githubusercontent.com/tatsu-lab/stanford_alpaca/main/alpaca_data.json
 ```
 
 * `samsum_dataset`
@@ -132,7 +132,7 @@ In case you are dealing with slower interconnect network between nodes, to reduc
 
 HSDP (Hybrid sharding Data Parallel) helps to define a hybrid sharding strategy where you can have FSDP within `sharding_group_size` which can be the minimum number of GPUs you can fit your model and DDP between the replicas of the model specified by `replica_group_size`.
 
-This will require to set the Sharding strategy in [fsdp config](../../src/llama_recipes/configs/fsdp.py) to `ShardingStrategy.HYBRID_SHARD` and specify two additional settings, `sharding_group_size` and `replica_group_size` where former specifies the sharding group size, number of GPUs that you model can fit into to form a replica of a model and latter specifies the replica group size, which is world_size/sharding_group_size.
+This will require to set the Sharding strategy in [fsdp config](../../src/llama_cookbook/configs/fsdp.py) to `ShardingStrategy.HYBRID_SHARD` and specify two additional settings, `sharding_group_size` and `replica_group_size` where former specifies the sharding group size, number of GPUs that you model can fit into to form a replica of a model and latter specifies the replica group size, which is world_size/sharding_group_size.
 
 ```bash
 

getting-started/finetuning/singlegpu_finetuning.md

@@ -1,12 +1,12 @@
 # Fine-tuning with Single GPU
 This recipe steps you through how to finetune a Meta Llama 3 model on the text summarization task using the [samsum](https://huggingface.co/datasets/samsum) dataset on a single GPU.
 
-These are the instructions for using the canonical [finetuning script](../../src/llama_recipes/finetuning.py) in the llama-recipes package.
+These are the instructions for using the canonical [finetuning script](../../src/llama_cookbook/finetuning.py) in the llama-cookbook package.
 
 
 ## Requirements
 
-Ensure that you have installed the llama-recipes package.
+Ensure that you have installed the llama-cookbook package.
 
 To run fine-tuning on a single GPU, we will make use of two packages:
 1. [PEFT](https://github.com/huggingface/peft) to use parameter-efficient finetuning.
@@ -33,15 +33,15 @@ The args used in the command above are:
 
 ### How to run with different datasets?
 
-Currently 3 open source datasets are supported that can be found in [Datasets config file](../../src/llama_recipes/configs/datasets.py). You can also use your custom dataset (more info [here](./datasets/README.md)).
+Currently 3 open source datasets are supported that can be found in [Datasets config file](../../src/llama_cookbook/configs/datasets.py). You can also use your custom dataset (more info [here](./datasets/README.md)).
 
-* `grammar_dataset` : use this [notebook](../../src/llama_recipes/datasets/grammar_dataset/grammar_dataset_process.ipynb) to pull and process the Jfleg and C4 200M datasets for grammar checking.
+* `grammar_dataset` : use this [notebook](../../src/llama_cookbook/datasets/grammar_dataset/grammar_dataset_process.ipynb) to pull and process the Jfleg and C4 200M datasets for grammar checking.
 
 * `alpaca_dataset` : to get this open source data please download the `alpaca.json` to `dataset` folder.
 
 
 ```bash
-wget -P ../../src/llama_recipes/datasets https://raw.githubusercontent.com/tatsu-lab/stanford_alpaca/main/alpaca_data.json
+wget -P ../../src/llama_cookbook/datasets https://raw.githubusercontent.com/tatsu-lab/stanford_alpaca/main/alpaca_data.json
 ```
 
 * `samsum_dataset`