diff --git a/README.md b/README.md
index 1da6194..1a1b2cc 100644
--- a/README.md
+++ b/README.md
@@ -75,13 +75,34 @@ def print_prime(n):
 ```
 where the model generates the text after the comments.
 
-**Notes**
+**Notes:**
 * Phi-1.5 is intended for research purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
 * Direct adoption for production tasks is out of the scope of this research project. As a result, Phi-1.5 has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
 * If you are using `transformers>=4.36.0`, always load the model with `trust_remote_code=True` to prevent side-effects.
 
 ## Sample Code
 
+There are four types of execution mode:
+
+1. FP16 / Flash-Attention / CUDA:
+   ```python
+   model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype="auto", flash_attn=True, flash_rotary=True, fused_dense=True, device_map="cuda", trust_remote_code=True)
+   ```
+2. FP16 / CUDA:
+   ```python
+   model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype="auto", device_map="cuda", trust_remote_code=True)
+   ```
+3. FP32 / CUDA:
+   ```python
+   model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype=torch.float32, device_map="cuda", trust_remote_code=True)
+   ```
+4. FP32 / CPU:
+   ```python
+   model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True)
+   ```
+
+To ensure the maximum compatibility, we recommend using the second execution mode (FP16 / CUDA), as follows:
+
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -91,8 +112,7 @@ torch.set_default_device("cuda")
 model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype="auto", trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True)
 
-inputs = tokenizer('''```python
-def print_prime(n):
+inputs = tokenizer('''def print_prime(n):
    """
    Print all primes between 1 and n
    """''', return_tensors="pt", return_attention_mask=False)
@@ -102,9 +122,10 @@ text = tokenizer.batch_decode(outputs)[0]
 print(text)
 ```
 
-**Remark.** In the generation function, our model currently does not support beam search (`num_beams > 1`). 
+**Remark:** In the generation function, our model currently does not support beam search (`num_beams > 1`).
 Furthermore, in the forward pass of the model, we currently do not support outputting hidden states or attention values, or using custom input embeddings.
 
+
 ## Limitations of Phi-1.5
 
 * Generate Inaccurate Code and Facts: The model often produces incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.