Merge branch 'main' into chester-azure-update

.github/scripts/spellcheck_conf/wordlist.txt

@@ -1432,4 +1432,4 @@ CPUs
 modelUpgradeExample
 guardrailing
 MaaS
-
+MFU

README.md

@@ -1,6 +1,6 @@
 # Llama Recipes: Examples to get started using the Llama models from Meta
 <!-- markdown-link-check-disable -->
-The 'llama-recipes' repository is a companion to the [Meta Llama](https://github.com/meta-llama/llama-models) models. We support the latest version, [Llama 3.1](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run Llama locally, in the cloud, and on-prem. 
+The 'llama-recipes' repository is a companion to the [Meta Llama](https://github.com/meta-llama/llama-models) models. We support the latest version, [Llama 3.1](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run Llama locally, in the cloud, and on-prem.
 
 <!-- markdown-link-check-enable -->
 > [!IMPORTANT]
@@ -31,7 +31,7 @@ The 'llama-recipes' repository is a companion to the [Meta Llama](https://github
 > ```
 > Each message gets trailed by an `<|eot_id|>` token before a new header is started, signaling a role change.
 >
-> More details on the new tokenizer and prompt template can be found [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1). 
+> More details on the new tokenizer and prompt template can be found [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1).
 
 >
 > [!NOTE]
@@ -55,6 +55,7 @@ The 'llama-recipes' repository is a companion to the [Meta Llama](https://github
   - [Repository Organization](#repository-organization)
     - [`recipes/`](#recipes)
     - [`src/`](#src)
+  - [Supported Features](#supported-features)
   - [Contributing](#contributing)
   - [License](#license)
 
@@ -160,6 +161,30 @@ Contains modules which support the example recipes:
 | [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.

recipes/README.md

@@ -4,8 +4,8 @@ This folder contains examples organized by topic:
 
 | Subfolder | Description |
 |---|---|
-[quickstart](./quickstart)|The "Hello World" of using Llama 3, start here if you are new to using Llama 3
-[use_cases](./use_cases)|Scripts showing common applications of Llama 3
-[3p_integrations](./3p_integrations)|Partner-owned folder showing Meta Llama 3 usage along with third-party tools 
+[quickstart](./quickstart)|The "Hello World" of using Llama, start here if you are new to using Llama
+[use_cases](./use_cases)|Scripts showing common applications of Llama
+[3p_integrations](./3p_integrations)|Partner-owned folder showing Llama usage along with third-party tools
 [responsible_ai](./responsible_ai)|Scripts to use PurpleLlama for safeguarding model outputs
-[experimental](./experimental)|Meta Llama implementations of experimental LLM techniques
+[experimental](./experimental)| Llama implementations of experimental LLM techniques

recipes/quickstart/README.md

@@ -2,28 +2,8 @@
 
 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.
 
-* The [Running_Llama3_Anywhere](./Running_Llama3_Anywhere/) notebooks demonstrate how to run Llama inference across Linux, Mac and Windows platforms using the appropriate tooling.
-* The [Prompt_Engineering_with_Llama_3](./Prompt_Engineering_with_Llama_3.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters.
+* The [Running_Llama_Anywhere](./Running_Llama3_Anywhere/) notebooks demonstrate how to run Llama inference across Linux, Mac and Windows platforms using the appropriate tooling.
+* The [Prompt_Engineering_with_Llama](./Prompt_Engineering_with_Llama_3.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters.
 * The [inference](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [3p_integrations/vllm](../3p_integrations/vllm/) and [3p_integrations/tgi](../3p_integrations/tgi/) for hosting Llama on open-source model servers.
-* The [RAG](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama 3.
-* The [finetuning](./finetuning/) folder contains resources to help you finetune Llama 3 on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-recipes finetuning code found in [finetuning.py](../../src/llama_recipes/finetuning.py) which supports these features:
-
-| Feature                                        |   |
-| ---------------------------------------------- | - |
-| HF support for finetuning                      | ✅ |
-| Deferred initialization ( meta init)           | ✅ |
-| HF support for inference                       | ✅ |
-| Low CPU mode for multi GPU                     | ✅ |
-| Mixed precision                                | ✅ |
-| Single node quantization                       | ✅ |
-| Flash attention                                | ✅ |
-| PEFT                                           | ✅ |
-| 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                         | ✅ |
+* The [RAG](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama.
+* The [finetuning](./finetuning/) folder contains resources to help you finetune Llama on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-recipes finetuning code found in [finetuning.py](../../src/llama_recipes/finetuning.py) which supports these features: