Improve setup guide

README.md

@@ -20,7 +20,14 @@ Optimum ExecuTorch enables efficient deployment of transformer models using Meta
 
 ## ⚡ Quick Installation
 
-Install from source:
+### 1. Create a virtual environment
+Install [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) on your machine. Then, create a virtual environment to manage our dependencies.
+```
+conda create -n optimum-executorch python=3.11
+conda activate optimum-executorch
+```
+
+### 2. Install optimum-executorch from source
 ```
 git clone https://github.com/huggingface/optimum-executorch.git
 cd optimum-executorch
@@ -29,11 +36,64 @@ pip install .
 
 - 🔜 Install from pypi coming soon...
 
+### [Optional] 3. Install dependencies in dev mode
+You can install `executorch` and `transformers` from source, where you can access new ExecuTorch
+compatilbe models from `transformers` and new features from `executorch` as both repos are under
+rapid deployment.
+
+Follow these steps manually:
+
+#### 3.1. Clone and Install ExecuTorch from Source
+From the root directory where `optimum-executorch` is cloned:
+```
+# Clone the ExecuTorch repository
+git clone https://github.com/pytorch/executorch.git
+cd executorch
+# Checkout the stable branch to ensure stability
+git checkout viable/strict
+# Install ExecuTorch
+bash ./install_executorch.sh
+cd ..
+```
+
+#### 3.2. Clone and Install Transformers from Source
+From the root directory where `optimum-executorch` is cloned:
+```
+# Clone the Transformers repository
+git clone https://github.com/huggingface/transformers.git
+cd transformers
+# Install Transformers in editable mode
+pip install -e .
+cd ..
+```
+
 ## 🎯 Quick Start
 
 There are two ways to use Optimum ExecuTorch:
 
-### Option 1: Export and Load Separately
+### Option 1: Export and Load in One Python API
+```python
+from optimum.executorch import ExecuTorchModelForCausalLM
+from transformers import AutoTokenizer
+
+# Load and export the model on-the-fly
+model_id = "meta-llama/Llama-3.2-1B"
+model = ExecuTorchModelForCausalLM.from_pretrained(model_id, recipe="xnnpack")
+
+# Generate text right away
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+generated_text = model.text_generation(
+    tokenizer=tokenizer,
+    prompt="Simply put, the theory of relativity states that",
+    max_seq_len=128
+)
+print(generated_text)
+```
+
+> **Note:** If an ExecuTorch model is already cached on the Hugging Face Hub, the API will automatically skip the export step and load the cached `.pte` file. To test this, replace the `model_id` in the example above with `"executorch-community/SmolLM2-135M"`, where the `.pte` file is pre-cached. Additionally, the `.pte` file can be directly associated with the eager model, as demonstrated in this [example](https://huggingface.co/optimum-internal-testing/tiny-random-llama/tree/executorch).
+
+
+### Option 2: Export and Load Separately
 
 #### Step 1: Export your model
 Use the CLI tool to convert your model to ExecuTorch format:
@@ -61,33 +121,35 @@ generated_text = model.text_generation(
     prompt="Simply put, the theory of relativity states that",
     max_seq_len=128
 )
+print(generated_text)
 ```
 
-### Option 2: Python API
-```python
-from optimum.executorch import ExecuTorchModelForCausalLM
-from transformers import AutoTokenizer
+## Supported Models and Backend
 
-# Load and export the model on-the-fly
-model_id = "meta-llama/Llama-3.2-1B"
-model = ExecuTorchModelForCausalLM.from_pretrained(model_id, recipe="xnnpack")
+**Optimum-ExecuTorch** currently supports the following transformer models:
+
+- [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) and its variants
+- [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) and its variants
+- [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) and its variants
+- [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) and its variants
+- [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) and its variants
+- [allenai/OLMo-1B-hf](https://huggingface.co/allenai/OLMo-1B-hf) and its variants
+
+*Note: This list is continuously expanding. As we continue to expand support, more models and variants will be added.*
+
+**Supported Backend:**
+
+Currently, **Optimum-ExecuTorch** supports only the [XNNPACK Backend](https://pytorch.org/executorch/main/backends-xnnpack.html) for efficient CPU execution on mobile devices. Quantization support for XNNPACK is planned to be added shortly.
+
+For a comprehensive overview of all backends supported by ExecuTorch, please refer to the [ExecuTorch Backend Overview](https://pytorch.org/executorch/main/backends-overview.html).
 
-# Generate text right away
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-generated_text = model.text_generation(
-    tokenizer=tokenizer,
-    prompt="Simply put, the theory of relativity states that",
-    max_seq_len=128
-)
-```
 
 ## 🛠️ Advanced Usage
 
 Check our [ExecuTorch GitHub repo](https://github.com/pytorch/executorch) directly for:
-- Custom model export configurations
-- Performance optimization guides
+- More backends and performance optimization options
 - Deployment guides for Android, iOS, and embedded devices
-- Additional examples
+- Additional examples and benchmarks
 
 ## 🤝 Contributing