move readme

README.md

@@ -1,4 +1,4 @@
-# Llama Recipes: Examples to get started using the Llama models from Meta
+# Llama Cookbook: The Official Guide to building with Llama Models
 <!-- markdown-link-check-disable -->
 
 > Note: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-recipes/tree/archive-main) is a snapshot branch from before the refactor
@@ -45,132 +45,21 @@ A: We recently did a refactor of the repo, [archive-main](https://github.com/met
 
 A: Official [Llama models website](https://www.llama.com)
 
-## Getting Started
+## Contributing
 
-These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
+Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.
 
-### Prerequisites
+## License
+<!-- markdown-link-check-disable -->
 
-#### PyTorch Nightlies
-If you want to use PyTorch nightlies instead of the stable release, go to [this guide](https://pytorch.org/get-started/locally/) to retrieve the right `--extra-index-url URL` parameter for the `pip install` commands on your platform.
+See the License file for Meta Llama 3.2 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/USE_POLICY.md)
 
-### Installing
-Llama-recipes provides a pip distribution for easy install and usage in other projects. Alternatively, it can be installed from source.
+See the License file for Meta Llama 3.1 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md)
 
-> [!NOTE]
-> Ensure you use the correct CUDA version (from `nvidia-smi`) when installing the PyTorch wheels. Here we are using 11.8 as `cu118`.
-> H100 GPUs work better with CUDA >12.0
-
-#### Install with pip
-```
-pip install llama-recipes
-```
-
-#### Install with optional dependencies
-Llama-recipes offers the installation of optional packages. There are three optional dependency groups.
-To run the unit tests we can install the required dependencies with:
-```
-pip install llama-recipes[tests]
-```
-For the vLLM example we need additional requirements that can be installed with:
-```
-pip install llama-recipes[vllm]
-```
-To use the sensitive topics safety checker install with:
-```
-pip install llama-recipes[auditnlg]
-```
-Some recipes require the presence of langchain. To install the packages follow the recipe description or install with:
-```
-pip install llama-recipes[langchain]
-```
-Optional dependencies can also be combined with [option1,option2].
-
-#### Install from source
-To install from source e.g. for development use these commands. We're using hatchling as our build backend which requires an up-to-date pip as well as setuptools package.
-```
-git clone git@github.com:meta-llama/llama-recipes.git
-cd llama-recipes
-pip install -U pip setuptools
-pip install -e .
-```
-For development and contributing to llama-recipes please install all optional dependencies:
-```
-git clone git@github.com:meta-llama/llama-recipes.git
-cd llama-recipes
-pip install -U pip setuptools
-pip install -e .[tests,auditnlg,vllm]
-```
-
-
-### Getting the Llama models
-You can find Llama models on Hugging Face hub [here](https://huggingface.co/meta-llama), **where models with `hf` in the name are already converted to Hugging Face checkpoints so no further conversion is needed**. The conversion step below is only for original model weights from Meta that are hosted on Hugging Face model hub as well.
-
-#### Model conversion to Hugging Face
-If you have the model checkpoints downloaded from the Meta website, you can convert it to the Hugging Face format with:
-
-```bash
-## Install Hugging Face Transformers from source
-pip freeze | grep transformers ## verify it is version 4.45.0 or higher
-
-git clone git@github.com:huggingface/transformers.git
-cd transformers
-pip install protobuf
-python src/transformers/models/llama/convert_llama_weights_to_hf.py \
-   --input_dir /path/to/downloaded/llama/weights --model_size 3B --output_dir /output/path
-```
-
-
-
-## Repository Organization
-Most of the code dealing with Llama usage is organized across 2 main folders: `recipes/` and `src/`.
-
-### `recipes/`
-
-Contains examples organized in folders by topic:
-| Subfolder | Description |
-|---|---|
-[quickstart](./recipes/quickstart) | The "Hello World" of using Llama, start here if you are new to using Llama.
-[use_cases](./recipes/use_cases)|Scripts showing common applications of Meta Llama3
-[3p_integrations](./recipes/3p_integrations)|Partner owned folder showing common applications of Meta Llama3
-[responsible_ai](./recipes/responsible_ai)|Scripts to use PurpleLlama for safeguarding model outputs
-[experimental](./recipes/experimental)|Meta Llama implementations of experimental LLM techniques
-
-### `src/`
-
-Contains modules which support the example recipes:
-| Subfolder | Description |
-|---|---|
-| [configs](src/llama_recipes/configs/) | Contains the configuration files for PEFT methods, FSDP, Datasets, Weights & Biases experiment tracking. |
-| [datasets](src/llama_recipes/datasets/) | Contains individual scripts for each dataset to download and process. Note |
-| [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. |
-| [policies](src/llama_recipes/policies/) | Contains FSDP scripts to provide different policies, such as mixed precision, transformer wrapping policy and activation checkpointing along with any precision optimizer (used for running FSDP with pure bf16 mode). |
-| [utils](src/llama_recipes/utils/) | Utility files for:<br/> - `train_utils.py` provides training/eval loop and more train utils.<br/> - `dataset_utils.py` to get preprocessed datasets.<br/> - `config_utils.py` to override the configs received from CLI.<br/> - `fsdp_utils.py` provides FSDP  wrapping policy for PEFT methods.<br/> - `memory_utils.py` context manager to track different memory stats in train loop. |
-
-
-## Supported Features
-The recipes and modules in this repository support the following features:
-
-| Feature                                        |   |
-| ---------------------------------------------- | - |
-| HF support for inference                       | ✅ |
-| HF support for finetuning                      | ✅ |
-| PEFT                                           | ✅ |
-| Deferred initialization ( meta init)           | ✅ |
-| Low CPU mode for multi GPU                     | ✅ |
-| Mixed precision                                | ✅ |
-| Single node quantization                       | ✅ |
-| Flash attention                                | ✅ |
-| Activation checkpointing FSDP                  | ✅ |
-| Hybrid Sharded Data Parallel (HSDP)            | ✅ |
-| Dataset packing & padding                      | ✅ |
-| BF16 Optimizer (Pure BF16)                     | ✅ |
-| Profiling & MFU tracking                       | ✅ |
-| Gradient accumulation                          | ✅ |
-| CPU offloading                                 | ✅ |
-| FSDP checkpoint conversion to HF for inference | ✅ |
-| W&B experiment tracker                         | ✅ |
+See the License file for Meta Llama 3 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3/USE_POLICY.md)
+
+See the License file for Meta Llama 2 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama2/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama2/USE_POLICY.md)
+<!-- markdown-link-check-enable -->
 
 
 ## Contributing

src/README.md

@@ -1,50 +1,3 @@
-# Llama Recipes: Examples to get started using the Llama models from Meta
-<!-- markdown-link-check-disable -->
-
-> Note: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-recipes/tree/archive-main) is a snapshot branch from before the refactor
-
-Welcome to the official repository for helping you get started with [inference](./getting-started/inference/), [fine-tuning](./getting-started/finetuning) and [end-to-end use-cases](./end-to-end-use-cases) of building with the Llama Model family.
-
-The examples cover the most popular community approaches, popular use-cases and the latest Llama 3.2 Vision and Llama 3.2 Text, in this repository. 
-
-> [!TIP]
-> Repository Structure:
-> * [Start building with the Llama 3.2 models](./getting-started/)
-> * [End to End Use cases with Llama model family](./end-to-end-use-cases)
-> * [Examples of building with 3rd Party Llama Providers](./3p-integrations)
-> * [Model Benchmarks](./benchmarks)
-
-> [!TIP]
-> Get started with Llama 3.2 with these new recipes:
-> * [Finetune Llama 3.2 Vision](./getting-started/finetuning/finetune_vision_model.md)
-> * [Multimodal Inference with Llama 3.2 Vision](./getting-started/inference/local_inference/README.md#multimodal-inference)
-> * [Inference on Llama Guard 1B + Multimodal inference on Llama Guard 11B-Vision](./end-to-end-use-cases/responsible_ai/llama_guard/llama_guard_text_and_vision_inference.ipynb)
-
-<!-- markdown-link-check-enable -->
-> [!NOTE]
-> Llama 3.2 follows the same prompt template as Llama 3.1, with a new special token `<|image|>` representing the input image for the multimodal models.
-> 
-> More details on the prompt templates for image reasoning, tool-calling and code interpreter can be found [on the documentation website](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_2).
-
-
-## Repository Structure:
-
-- [3P Integrations](./3p-integrations): Getting Started Recipes and End to End Use-Cases from various Llama providers
-- [End to End Use Cases](./end-to-end-use-cases): As the name suggests, spanning various domains and applications
-- [Getting Started](./getting-started/): Reference for inferencing, fine-tuning and RAG examples
-- [Benchmarks](./benchmarks): Reference implementation for some benchmarks
-
-
-## FAQ: 
-
-- Q: Some links are broken/folders are missing: 
-
-A: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-recipes/tree/archive-main) is a snapshot branch from before the refactor
-
-- Where can we find details about the latest models?
-
-A: Official [Llama models website](https://www.llama.com)
-
 ## Getting Started
 
 These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
@@ -118,73 +71,4 @@ cd transformers
 pip install protobuf
 python src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir /path/to/downloaded/llama/weights --model_size 3B --output_dir /output/path
-```
-
-
-
-## Repository Organization
-Most of the code dealing with Llama usage is organized across 2 main folders: `recipes/` and `src/`.
-
-### `recipes/`
-
-Contains examples organized in folders by topic:
-| Subfolder | Description |
-|---|---|
-[quickstart](./recipes/quickstart) | The "Hello World" of using Llama, start here if you are new to using Llama.
-[use_cases](./recipes/use_cases)|Scripts showing common applications of Meta Llama3
-[3p_integrations](./recipes/3p_integrations)|Partner owned folder showing common applications of Meta Llama3
-[responsible_ai](./recipes/responsible_ai)|Scripts to use PurpleLlama for safeguarding model outputs
-[experimental](./recipes/experimental)|Meta Llama implementations of experimental LLM techniques
-
-### `src/`
-
-Contains modules which support the example recipes:
-| Subfolder | Description |
-|---|---|
-| [configs](src/llama_recipes/configs/) | Contains the configuration files for PEFT methods, FSDP, Datasets, Weights & Biases experiment tracking. |
-| [datasets](src/llama_recipes/datasets/) | Contains individual scripts for each dataset to download and process. Note |
-| [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. |
-| [policies](src/llama_recipes/policies/) | Contains FSDP scripts to provide different policies, such as mixed precision, transformer wrapping policy and activation checkpointing along with any precision optimizer (used for running FSDP with pure bf16 mode). |
-| [utils](src/llama_recipes/utils/) | Utility files for:<br/> - `train_utils.py` provides training/eval loop and more train utils.<br/> - `dataset_utils.py` to get preprocessed datasets.<br/> - `config_utils.py` to override the configs received from CLI.<br/> - `fsdp_utils.py` provides FSDP  wrapping policy for PEFT methods.<br/> - `memory_utils.py` context manager to track different memory stats in train loop. |
-
-
-## Supported Features
-The recipes and modules in this repository support the following features:
-
-| Feature                                        |   |
-| ---------------------------------------------- | - |
-| HF support for inference                       | ✅ |
-| HF support for finetuning                      | ✅ |
-| PEFT                                           | ✅ |
-| Deferred initialization ( meta init)           | ✅ |
-| Low CPU mode for multi GPU                     | ✅ |
-| Mixed precision                                | ✅ |
-| Single node quantization                       | ✅ |
-| Flash attention                                | ✅ |
-| Activation checkpointing FSDP                  | ✅ |
-| Hybrid Sharded Data Parallel (HSDP)            | ✅ |
-| Dataset packing & padding                      | ✅ |
-| BF16 Optimizer (Pure BF16)                     | ✅ |
-| Profiling & MFU tracking                       | ✅ |
-| Gradient accumulation                          | ✅ |
-| CPU offloading                                 | ✅ |
-| FSDP checkpoint conversion to HF for inference | ✅ |
-| W&B experiment tracker                         | ✅ |
-
-
-## Contributing
-
-Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.
-
-## License
-<!-- markdown-link-check-disable -->
-
-See the License file for Meta Llama 3.2 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/USE_POLICY.md)
-
-See the License file for Meta Llama 3.1 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md)
-
-See the License file for Meta Llama 3 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3/USE_POLICY.md)
-
-See the License file for Meta Llama 2 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama2/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama2/USE_POLICY.md)
-<!-- markdown-link-check-enable -->
+```