Update README.md

README.md

@@ -17,7 +17,6 @@ Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the
 
 Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
 
-
 ## Intended Uses
 
 Phi-2 is intended for research purposes only. Given the nature of the training data, the Phi-2 model is best suited for prompts using the QA format, the chat format, and the code format.
@@ -69,13 +68,34 @@ def print_prime(n):
 ```
 where the model generates the text after the comments.
 
-**Notes**
+**Notes:**
 * Phi-2 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, the Phi-2 model 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-2", 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-2", torch_dtype="auto", device_map="cuda", trust_remote_code=True)
+   ```
+3. FP32 / CUDA:
+   ```python
+   model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float32, device_map="cuda", trust_remote_code=True)
+   ```
+4. FP32 / CPU:
+   ```python
+   model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", 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
@@ -85,8 +105,7 @@ torch.set_default_device("cuda")
 model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", 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)
@@ -96,7 +115,7 @@ 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-2