Added install section to readme

README.md

@@ -22,6 +22,21 @@ Llama 2 is a new technology that carries potential risks with use. Testing condu
 
 [Llama 2 Jupyter Notebook](quickstart.ipynb): This jupyter notebook steps you through how to finetune a Llama 2 model on the text summarization task using the [samsum](https://huggingface.co/datasets/samsum). The notebook uses parameter efficient finetuning (PEFT) and int8 quantization to finetune a 7B on a single GPU like an A10 with 24GB gpu memory.
 
+# Installation
+Llama-recipes provides a pip distribution for easy install and usage in other projects. Alternatively, it can be installed from source.
+
+## Install with pip
+```
+pip install llama-recipes
+```
+## Install from source
+To install from source e.g. for development use this command. We're using hatchling as our build backend which requires an up-to-date pip as well as setuptools package.
+```
+pip install -U pip setuptools
+pip install -e .
+```
+⚠️ **Note** ⚠️  Some features (especially fine-tuning with FSDP + PEFT) currently require PyTorch nightlies to be installed. Please make sure to install the nightlies if you're using these features following [this guide](https://pytorch.org/get-started/locally/).
+
 **Note** All the setting defined in [config files](./configs/) can be passed as args through CLI when running the script, there is no need to change from config files directly.
 
 **Note** In case need to run PEFT model with FSDP, please make sure to use the PyTorch Nightlies.
@@ -35,17 +50,6 @@ Llama 2 is a new technology that carries potential risks with use. Testing condu
 * [Inference](./docs/inference.md)
 * [FAQs](./docs/FAQ.md)
 
-## Requirements
-To run the examples, make sure to install the requirements using
-
-```bash
-# python 3.9 or higher recommended
-pip install -r requirements.txt
-
-```
-
-**Please note that the above requirements.txt will install PyTorch 2.0.1 version, in case you want to run FSDP + PEFT, please make sure to install PyTorch nightlies.**
-
 # Where to find the models?
 
 You can find llama v2 models on HuggingFace hub [here](https://huggingface.co/meta-llama), where models with `hf` in the name are already converted to HuggingFace checkpoints so no further conversion is needed. The conversion step below is only for original model weights from Meta that are hosted on HuggingFace model hub as well.