Fix more package naming (#864)

3p-integrations/crusoe/vllm-fp8/README.md

@@ -23,8 +23,8 @@ source $HOME/.cargo/env
 
 Now, clone the recipes and navigate to this tutorial. Initialize the virtual environment and install dependencies:
 ```bash
-git clone https://github.com/meta-llama/llama-recipes.git
-cd llama-recipes/recipes/3p_integrations/crusoe/vllm-fp8/
+git clone https://github.com/meta-llama/llama-cookbook.git
+cd llama-cookbook/recipes/3p_integrations/crusoe/vllm-fp8/
 uv add vllm setuptools
 ```
 

3p-integrations/llama_on_prem.md

@@ -1,6 +1,6 @@
 # Llama 3 On-Prem Inference Using vLLM and TGI
 
-Enterprise customers may prefer to deploy Llama 3 on-prem and run Llama in their own servers. This tutorial shows how to use Llama 3 with [vLLM](https://github.com/vllm-project/vllm) and Hugging Face [TGI](https://github.com/huggingface/text-generation-inference), two leading open-source tools to deploy and serve LLMs, and how to create vLLM and TGI hosted Llama 3 instances with [LangChain](https://www.langchain.com/), an open-source LLM app development framework which we used for our other demo apps: [Getting to Know Llama](../getting-started/build_with_Llama_3_2.ipynb), Running Llama 3 <!-- markdown-link-check-disable -->[locally](https://github.com/meta-llama/llama-recipes/blob/main/recipes/quickstart/Running_Llama3_Anywhere/Running_Llama_on_Mac_Windows_Linux.ipynb) <!-- markdown-link-check-disable --> and [in the cloud](https://github.com/meta-llama/llama-recipes/blob/main/recipes/quickstart/RAG/hello_llama_cloud.ipynb). See [here](https://medium.com/@rohit.k/tgi-vs-vllm-making-informed-choices-for-llm-deployment-37c56d7ff705) for a detailed comparison of vLLM and TGI.
+Enterprise customers may prefer to deploy Llama 3 on-prem and run Llama in their own servers. This tutorial shows how to use Llama 3 with [vLLM](https://github.com/vllm-project/vllm) and Hugging Face [TGI](https://github.com/huggingface/text-generation-inference), two leading open-source tools to deploy and serve LLMs, and how to create vLLM and TGI hosted Llama 3 instances with [LangChain](https://www.langchain.com/), an open-source LLM app development framework which we used for our other demo apps: [Getting to Know Llama](../getting-started/build_with_Llama_3_2.ipynb), Running Llama 3 <!-- markdown-link-check-disable -->[locally](https://github.com/meta-llama/llama-cookbook/blob/main/recipes/quickstart/Running_Llama3_Anywhere/Running_Llama_on_Mac_Windows_Linux.ipynb) <!-- markdown-link-check-disable --> and [in the cloud](https://github.com/meta-llama/llama-cookbook/blob/main/recipes/quickstart/RAG/hello_llama_cloud.ipynb). See [here](https://medium.com/@rohit.k/tgi-vs-vllm-making-informed-choices-for-llm-deployment-37c56d7ff705) for a detailed comparison of vLLM and TGI.
 
 For [Ollama](https://ollama.com) based on-prem inference with Llama 3, see the Running Llama 3 locally notebook above.
 

3p-integrations/tgi/README.md

@@ -9,7 +9,7 @@ In case the model was fine tuned with LoRA method we need to merge the weights o
 The script takes the base model, the peft weight folder as well as an output as arguments:
 
 ```
-python -m llama_recipes.recipes.3p_integration.tgi.merge_lora_weights --base_model llama-7B --peft_model ft_output --output_dir data/merged_model_output
+python -m llama_cookbook.recipes.3p_integration.tgi.merge_lora_weights --base_model llama-7B --peft_model ft_output --output_dir data/merged_model_output
 ```
 
 ## Step 1: Serving the model

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

@@ -116,7 +116,7 @@ As shown in the above example, we have a "question" section for the generated qu
 To create a reliable evaluation set, it's ideal to use human-annotated question and answer pairs. This ensures that the questions are relevant and the answers are accurate. However, human annotation is time-consuming and costly. For demonstration purposes, we'll use a subset of the validation set, which will never be used in the fine-tuning. We only need to keep the "question" section and the final answer section, marked by the `<ANSWER>` tag in "cot_answer". We'll manually check each example and select only the good ones. We want to ensure that the questions are general enough to be used for web search engine queries and are related to Llama. We'll also use some QA pairs from our FAQ page, with modifications. This will result in 72 question and answer pairs as our evaluation set, saved as `eval_llama.json`.
 
 ## Fine-Tuning Steps
-Once the RAFT dataset is ready in JSON format, we can start fine-tuning. Unfortunately, the LORA method didn't produce good results, so we'll use the full fine-tuning method. We can use the following commands as an example in the llama-recipes main folder:
+Once the RAFT dataset is ready in JSON format, we can start fine-tuning. Unfortunately, the LORA method didn't produce good results, so we'll use the full fine-tuning method. We can use the following commands as an example in the llama-cookbook main folder:
 
 ```bash
 export PATH_TO_ROOT_FOLDER=./raft-8b
@@ -129,7 +129,7 @@ For more details on multi-GPU fine-tuning, please refer to the [multigpu_finetun
 Next, we need to convert the FSDP checkpoint to a HuggingFace checkpoint using the following command:
 
 ```bash
-python src/llama_recipes/inference/checkpoint_converter_fsdp_hf.py --fsdp_checkpoint_path  "$PATH_TO_ROOT_FOLDER/fine-tuned-meta-Llama/Meta-Llama-3-8B-Instruct" --consolidated_model_path "$PATH_TO_ROOT_FOLDER"
+python src/llama_cookbook/inference/checkpoint_converter_fsdp_hf.py --fsdp_checkpoint_path  "$PATH_TO_ROOT_FOLDER/fine-tuned-meta-Llama/Meta-Llama-3-8B-Instruct" --consolidated_model_path "$PATH_TO_ROOT_FOLDER"
 ```
 
 For more details on FSDP to HuggingFace checkpoint conversion, please refer to the [readme](../../getting-started/finetuning/multigpu_finetuning.md) in the inference/local_inference recipe.

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/README.md

@@ -25,8 +25,8 @@ Given those differences, the numbers from this recipe can not be compared to the
 Please install lm-evaluation-harness and our llama-recipe repo by following:
 
 ```
-git clone git@github.com:meta-llama/llama-recipes.git
-cd llama-recipes
+git clone git@github.com:meta-llama/llama-cookbook.git
+cd llama-cookbook
 pip install -U pip setuptools
 pip install -e .
 pip install lm-eval[math,ifeval,sentencepiece,vllm]==0.4.3

end-to-end-use-cases/coding/text2sql/quickstart.ipynb

@@ -1,5 +1,5 @@
 {
- "cells": [
+ "cells": [llama-cookbook
   {
    "cell_type": "markdown",
    "id": "e8cba0b6",

end-to-end-use-cases/customerservice_chatbots/RAG_chatbot/RAG_Chatbot_Example.ipynb

@@ -402,7 +402,7 @@
     "In this example, we will be deploying a Meta Llama 3 8B chat HuggingFace model with the Text-generation-inference framework on-permises.  \n",
     "This would allow us to directly wire the API server with our chatbot.  \n",
     "There are alternative solutions to deploy Meta Llama 3 models on-permises as your local API server.  \n",
-    "You can find our complete guide [here](https://github.com/meta-llama/llama-recipes/blob/main/recipes/inference/model_servers/llama-on-prem.md)."
+    "You can find our complete guide [here](https://github.com/meta-llama/llama-cookbook/blob/main/recipes/inference/model_servers/llama-on-prem.md)."
    ]
   },
   {

end-to-end-use-cases/github_triage/README.md

@@ -32,7 +32,7 @@ pip install -r requirements.txt
 ### Running the Tool
 
 ```bash
-python triage.py --repo_name='meta-llama/llama-recipes' --start_date='2024-08-14' --end_date='2024-08-27'
+python triage.py --repo_name='meta-llama/llama-cookbook' --start_date='2024-08-14' --end_date='2024-08-27'
 ```
 
 ### Output

end-to-end-use-cases/github_triage/walkthrough.ipynb

@@ -1,4 +1,4 @@
-{
+{llama-cookbookllama-cookbook
   "cells": [
     {
       "cell_type": "code",

end-to-end-use-cases/responsible_ai/code_shield_usage_demo.ipynb

@@ -151,7 +151,7 @@
     "import os\n",
     "import getpass\n",
     "\n",
-    "from llama_recipes.inference.llm import TOGETHER, OPENAI, ANYSCALE\n",
+    "from llama_cookbook.inference.llm import TOGETHER, OPENAI, ANYSCALE\n",
     "\n",
     "if \"EXTERNALLY_HOSTED_LLM_TOKEN\" not in os.environ:\n",
     "    os.environ[\"EXTERNALLY_HOSTED_LLM_TOKEN\"] = getpass.getpass(prompt=\"Provide token for LLM provider\")\n",

end-to-end-use-cases/responsible_ai/llama_guard/README.md

@@ -6,7 +6,7 @@ This [notebook](llama_guard_text_and_vision_inference.ipynb) shows how to load t
 
 ## Requirements
 1. Access to Llama guard model weights on Hugging Face. To get access, follow the steps described in the top of the model card in [Hugging Face](https://huggingface.co/meta-llama/Llama-Guard-3-1B)
-2. Llama recipes package and its dependencies [installed](https://github.com/meta-llama/llama-recipes?tab=readme-ov-file#installing)
+2. Llama recipes package and its dependencies [installed](https://github.com/meta-llama/llama-cookbook?tab=readme-ov-file#installing)
 3. Pillow package installed
 
 ## Inference Safety Checker

end-to-end-use-cases/responsible_ai/llama_guard/llama_guard_customization_via_prompting_and_fine_tuning.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "Llama Guard is provided with a reference taxonomy explained on [this page](https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-guard-3), where the prompting format is also explained. \n",
     "\n",
-    "The functions below combine already existing [prompt formatting code in llama-recipes](https://github.com/meta-llama/llama-recipes/blob/main/src/llama_recipes/inference/prompt_format_utils.py) with custom code to aid in the custimization of the taxonomy. "
+    "The functions below combine already existing [prompt formatting code in llama-recipes](https://github.com/meta-llama/llama-recipes/blob/main/src/llama_cookbook/inference/prompt_format_utils.py) with custom code to aid in the custimization of the taxonomy. "
    ]
   },
   {
@@ -80,7 +80,7 @@
    ],
    "source": [
     "from enum import Enum\n",
-    "from llama_recipes.inference.prompt_format_utils import  LLAMA_GUARD_3_CATEGORY, SafetyCategory, AgentType\n",
+    "from llama_cookbook.inference.prompt_format_utils import  LLAMA_GUARD_3_CATEGORY, SafetyCategory, AgentType\n",
     "from typing import List\n",
     "\n",
     "class LG3Cat(Enum):\n",
@@ -158,7 +158,7 @@
     }
    ],
    "source": [
-    "from llama_recipes.inference.prompt_format_utils import build_custom_prompt, create_conversation, PROMPT_TEMPLATE_3, LLAMA_GUARD_3_CATEGORY_SHORT_NAME_PREFIX\n",
+    "from llama_cookbook.inference.prompt_format_utils import build_custom_prompt, create_conversation, PROMPT_TEMPLATE_3, LLAMA_GUARD_3_CATEGORY_SHORT_NAME_PREFIX\n",
     "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
     "from typing import List, Tuple\n",
     "from enum import Enum\n",
@@ -463,13 +463,13 @@
     "\n",
     "To add additional datasets\n",
     "\n",
-    "1. Copy llama-recipes/src/llama_recipes/datasets/toxicchat_dataset.py \n",
+    "1. Copy llama-recipes/src/llama_cookbook/datasets/toxicchat_dataset.py \n",
     "2. Modify the file to change the dataset used\n",
     "3. Add references to the new dataset in \n",
-    "    - llama-recipes/src/llama_recipes/configs/datasets.py\n",
-    "    - llama_recipes/datasets/__init__.py\n",
-    "    - llama_recipes/datasets/toxicchat_dataset.py\n",
-    "    - llama_recipes/utils/dataset_utils.py\n",
+    "    - llama-recipes/src/llama_cookbook/configs/datasets.py\n",
+    "    - llama_cookbook/datasets/__init__.py\n",
+    "    - llama_cookbook/datasets/toxicchat_dataset.py\n",
+    "    - llama_cookbook/utils/dataset_utils.py\n",
     "\n",
     "\n",
     "## Evaluation\n",
@@ -484,7 +484,7 @@
    "source": [
     "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
     "\n",
-    "from llama_recipes.inference.prompt_format_utils import build_default_prompt, create_conversation, LlamaGuardVersion\n",
+    "from llama_cookbook.inference.prompt_format_utils import build_default_prompt, create_conversation, LlamaGuardVersion\n",
     "from llama.llama.generation import Llama\n",
     "\n",
     "from typing import List, Optional, Tuple, Dict\n",
@@ -726,7 +726,7 @@
     "#     \"unsafe_content\": [\"O1\"]\n",
     "# }\n",
     "# ```\n",
-    "from llama_recipes.datasets.toxicchat_dataset import get_llamaguard_toxicchat_dataset\n",
+    "from llama_cookbook.datasets.toxicchat_dataset import get_llamaguard_toxicchat_dataset\n",
     "validation_data = get_llamaguard_toxicchat_dataset(None, None, \"train\", return_jsonl = True)[0:100]\n",
     "run_validation(validation_data, AgentType.USER, Type.HF, load_in_8bit = False, load_in_4bit = True)"
    ]
@@ -757,7 +757,7 @@
    "outputs": [],
    "source": [
     "model_id = \"meta-llama/Llama-Guard-3-8B\"\n",
-    "from llama_recipes import finetuning\n",
+    "from llama_cookbook import finetuning\n",
     "\n",
     "finetuning.main(\n",
     "    model_name = model_id,\n",

end-to-end-use-cases/responsible_ai/prompt_guard/README.md

@@ -8,4 +8,4 @@ This is a very small model and inference and fine-tuning are feasible on local C
 
 ## Requirements
 1. Access to Prompt Guard model weights on Hugging Face. To get access, follow the steps described [here](https://github.com/facebookresearch/PurpleLlama/tree/main/Prompt-Guard#download)
-2. Llama recipes package and it's dependencies [installed](https://github.com/meta-llama/llama-recipes?tab=readme-ov-file#installing)
+2. Llama recipes package and it's dependencies [installed](https://github.com/meta-llama/llama-cookbook?tab=readme-ov-file#installing)

getting-started/README.md

@@ -1,4 +1,4 @@
-## Llama-Recipes Getting Started
+## Llama-cookbook Getting Started
 
 If you are new to developing with Meta Llama models, this is where you should start. This folder contains introductory-level notebooks across different techniques relating to Meta Llama.
 

getting-started/finetuning/finetune_vision_model.md

@@ -1,7 +1,7 @@
 ## Llama 3.2 Vision Models Fine-Tuning Recipe
 This recipe steps you through how to finetune a Llama 3.2 vision model on the OCR VQA task using the [OCRVQA](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron/viewer/ocrvqa?row=0) dataset.
 
-**Disclaimer**: As our vision models already have a very good OCR ability, here we use the OCRVQA dataset only for demonstration purposes of the required steps for fine-tuning our vision models with llama-recipes.
+**Disclaimer**: As our vision models already have a very good OCR ability, here we use the OCRVQA dataset only for demonstration purposes of the required steps for fine-tuning our vision models with llama-cookbook.
 
 ### Fine-tuning steps
 

getting-started/finetuning/finetuning.py

@@ -2,7 +2,7 @@
 # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
 
 import fire
-from llama_recipes.finetuning import main
+from llama_cookbook.finetuning import main
 
 if __name__ == "__main__":
     fire.Fire(main)

getting-started/finetuning/quickstart_peft_finetuning.ipynb

@@ -31,17 +31,17 @@
    "source": [
     "### Step 0: Install pre-requirements and convert checkpoint\n",
     "\n",
-    "We need to have llama-recipes and its dependencies installed for this notebook. Additionally, we need to log in with the huggingface_cli and make sure that the account is able to to access the Meta Llama weights."
+    "We need to have llama-cookbook and its dependencies installed for this notebook. Additionally, we need to log in with the huggingface_cli and make sure that the account is able to to access the Meta Llama weights."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# uncomment if running from Colab T4\n",
-    "# ! pip install llama-recipes ipywidgets\n",
+    "# ! pip install llama-cookbook ipywidgets\n",
     "\n",
     "# import huggingface_hub\n",
     "# huggingface_hub.login()"
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
    "source": [
     "import torch\n",
     "from transformers import LlamaForCausalLM, AutoTokenizer\n",
-    "from llama_recipes.configs import train_config as TRAIN_CONFIG\n",
+    "from llama_cookbook.configs import train_config as TRAIN_CONFIG\n",
     "\n",
     "train_config = TRAIN_CONFIG()\n",
     "train_config.model_name = \"meta-llama/Meta-Llama-3.1-8B\"\n",
@@ -221,8 +221,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_recipes.configs.datasets import samsum_dataset\n",
-    "from llama_recipes.utils.dataset_utils import get_dataloader\n",
+    "from llama_cookbook.configs.datasets import samsum_dataset\n",
+    "from llama_cookbook.utils.dataset_utils import get_dataloader\n",
     "\n",
     "samsum_dataset.trust_remote_code = True\n",
     "\n",
@@ -248,7 +248,7 @@
    "source": [
     "from peft import get_peft_model, prepare_model_for_kbit_training, LoraConfig\n",
     "from dataclasses import asdict\n",
-    "from llama_recipes.configs import lora_config as LORA_CONFIG\n",
+    "from llama_cookbook.configs import lora_config as LORA_CONFIG\n",
     "\n",
     "lora_config = LORA_CONFIG()\n",
     "lora_config.r = 8\n",
@@ -278,7 +278,7 @@
    "outputs": [],
    "source": [
     "import torch.optim as optim\n",
-    "from llama_recipes.utils.train_utils import train\n",
+    "from llama_cookbook.utils.train_utils import train\n",
     "from torch.optim.lr_scheduler import StepLR\n",
     "\n",
     "model.train()\n",

getting-started/inference/local_inference/inference.py

@@ -10,9 +10,9 @@
 import torch
 
 from accelerate.utils import is_xpu_available
-from llama_recipes.inference.model_utils import load_model, load_peft_model
+from llama_cookbook.inference.model_utils import load_model, load_peft_model
 
-from llama_recipes.inference.safety_utils import AgentType, get_safety_checker
+from llama_cookbook.inference.safety_utils import AgentType, get_safety_checker
 from transformers import AutoTokenizer
 
 
@@ -176,7 +176,7 @@ def inference(
                 )
             ],
             title="Meta Llama3 Playground",
-            description="https://github.com/meta-llama/llama-recipes",
+            description="https://github.com/meta-llama/llama-cookbook",
         ).queue().launch(server_name="0.0.0.0", share=share_gradio)
 
 

getting-started/inference/mobile_inference/android_inference/README.md

@@ -103,7 +103,7 @@ Connect your phone to your development machine. On OSX, you'll be prompted on th
 
 ## Building the Android Package with MLC
 
-First edit the file under `android/MLCChat/mlc-package-config.json` and with the [mlc-package-config.json](./mlc-package-config.json) in llama-recipes.
+First edit the file under `android/MLCChat/mlc-package-config.json` and with the [mlc-package-config.json](./mlc-package-config.json) in llama-cookbook.
 
 To understand what these JSON fields mean you can refer to this [documentation](https://llm.mlc.ai/docs/deploy/android.html#step-2-build-runtime-and-model-libraries).
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "llama-cookbook"
-version = "0.0.5"
+version = "0.0.5.post1"
 authors = [
   { name="Hamid Shojanazeri", email="hamidnazeri@meta.com" },
   { name="Matthias Reso", email="mreso@meta.com" },

src/llama_cookbook/configs/datasets.py

@@ -14,16 +14,16 @@ class samsum_dataset:
 @dataclass
 class grammar_dataset:
     dataset: str = "grammar_dataset"
-    train_split: str = "src/llama_recipes/datasets/grammar_dataset/gtrain_10k.csv"
-    test_split: str = "src/llama_recipes/datasets/grammar_dataset/grammar_validation.csv"
+    train_split: str = "src/llama_cookbook/datasets/grammar_dataset/gtrain_10k.csv"
+    test_split: str = "src/llama_cookbook/datasets/grammar_dataset/grammar_validation.csv"
 
 
 @dataclass
 class alpaca_dataset:
     dataset: str = "alpaca_dataset"
     train_split: str = "train"
     test_split: str = "val"
-    data_path: str = "src/llama_recipes/datasets/alpaca_data.json"
+    data_path: str = "src/llama_cookbook/datasets/alpaca_data.json"
 
 @dataclass
 class custom_dataset:
@@ -32,7 +32,7 @@ class custom_dataset:
     train_split: str = "train"
     test_split: str = "validation"
     data_path: str = ""
-    
+
 @dataclass
 class llamaguard_toxicchat_dataset:
     dataset: str = "llamaguard_toxicchat_dataset"

src/llama_cookbook/configs/wandb.py

@@ -6,10 +6,10 @@
 
 @dataclass
 class wandb_config:
-    project: str = 'llama_recipes' # wandb project name
+    project: str = 'llama_cookbook' # wandb project name
     entity: Optional[str] = None # wandb entity name
     job_type: Optional[str] = None
     tags: Optional[List[str]] = None
     group: Optional[str] = None
     notes: Optional[str] = None
-    mode: Optional[str] = None
+    mode: Optional[str] = None