diff --git a/.github/scripts/spellcheck_conf/wordlist.txt b/.github/scripts/spellcheck_conf/wordlist.txt
index 8bbc8da4a..ec01ea247 100644
--- a/.github/scripts/spellcheck_conf/wordlist.txt
+++ b/.github/scripts/spellcheck_conf/wordlist.txt
@@ -1432,4 +1432,4 @@ CPUs
modelUpgradeExample
guardrailing
MaaS
-
+MFU
\ No newline at end of file
diff --git a/README.md b/README.md
index 007b3df37..aa08f1eef 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
# Llama Recipes: Examples to get started using the Llama models from Meta
-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.
> [!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:
- `train_utils.py` provides training/eval loop and more train utils.
- `dataset_utils.py` to get preprocessed datasets.
- `config_utils.py` to override the configs received from CLI.
- `fsdp_utils.py` provides FSDP wrapping policy for PEFT methods.
- `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.
diff --git a/recipes/README.md b/recipes/README.md
index 9b5234eec..86d90b7e0 100644
--- a/recipes/README.md
+++ b/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
diff --git a/recipes/quickstart/README.md b/recipes/quickstart/README.md
index 4c82bfbbd..326cbdb29 100644
--- a/recipes/quickstart/README.md
+++ b/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: