Update README.md

README.md

@@ -2,34 +2,27 @@
 inference: false
 license: other
 license_name: microsoft-research-license
-license_link: https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx
+license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
 language:
 - en
 pipeline_tag: text-generation
+tags:
+- nlp
+- code
 ---
 
 ## Model Summary
 
-Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as [phi-1.5](https://huggingface.co/microsoft/phi-1.5), augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters.
+Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as [Phi-1.5](https://huggingface.co/microsoft/phi-1.5), augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters.
 
 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
 
-Below are example codes to load phi-2, we support two modes of execution for the model:
- 1. loading in fp-16 format with flash-attention support:
-	 ```python
-	 model = AutoModelForCausalLM.from_pretrained('microsoft/phi-2', torch_dtype='auto', flash_attn=True, flash_rotary=True, fused_dense=True, trust_remote_code=True)
-	 ```
- 2. loading in fp-16 without flash-attention
-	 ```python
-	 model = AutoModelForCausalLM.from_pretrained('microsoft/phi-2', torch_dtype='auto', trust_remote_code=True)
-	 ```
+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.
 
-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.
-
-#### QA format:
+### QA Format:
 
 You can provide the prompt as a standalone question as follows:
 
@@ -44,7 +37,8 @@ Output: Mathematics is like a lighthouse. Just as a lighthouse guides ships safe
 ```
 where the model generates the text after "Output:".
 
-#### Chat format:
+### Chat Format:
+
 ```markdown
 Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
 Bob: Well, have you tried creating a study schedule and sticking to it?
@@ -55,7 +49,8 @@ Alice: ...
 
 where the model generates the text after the first "Bob:".
 
-#### Code format:
+### Code Format:
+
 ```python
 def print_prime(n):
    """
@@ -74,34 +69,52 @@ def print_prime(n):
 ```
 where the model generates the text after the comments.
 
-  
-**Out of scope**
+**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.
 
-* The phi-2 model 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.
+## Sample Code
 
-* 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.
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
+torch.set_default_device("cuda")
 
-## Limitations of phi-2
+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):
+   """
+   Print all primes between 1 and n
+   """''', return_tensors="pt", return_attention_mask=False)
+
+outputs = model.generate(**inputs, max_length=200)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+
+**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
 
 * Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
 
-* Limited Scope for code: Majority of phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
+* Limited Scope for code: Majority of Phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
 
 * Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
 
 * Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
 
-* Potential Societal Biases: phi-2 is not entirely free from societal biases despite efforts in assuring trainig data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
+* Potential Societal Biases: Phi-2 is not entirely free from societal biases despite efforts in assuring trainig data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
 
 * Toxicity: Despite being trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
 
 * Verbosity: Phi-2 being a base model often produces irrelevant or extra text and responses following its first answer to user prompts within a single turn. This is due to its training dataset being primarily textbooks, which results in textbook-like responses.
 
-  
-
 ## Training
 
 ### Model
@@ -118,19 +131,17 @@ where the model generates the text after the comments.
 
 * Training time: 14 days
 
-  
-
 ### Software
 
 * [PyTorch](https://github.com/pytorch/pytorch)
 
 * [DeepSpeed](https://github.com/microsoft/DeepSpeed)
 
-* [flash-attention](https://github.com/HazyResearch/flash-attention) > 2.0.0
+* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
 
 ### License
 
-The model is licensed under the [microsoft-research-license](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx).
+The model is licensed under the [microsoft-research-license](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE).
 
 ## Trademarks